BotBR: Social Bot Detection with Balanced Feature Fusion and Reliability-Enhanced Graph Learning

The rise of social bots poses a significant threat to online platforms, making their detection an urgent priority.Recent advancements in Graph Neural Networks (GNNs) have significantly improved bot detection by leveraging the rich relational data within social networks.However, existing approaches face two key challenges: imbalanced feature fusion across different modalities and edge heterophily, which limit their effectiveness.To address these issues, we propose BotBR, a novel bot detection framework.BotBR tackles feature imbalance by employing decision trees to extract behavioral patterns from user-related numerical features and utilizes an attention mechanism to seamlessly integrate multi-modal feature embeddings.To mitigate edge heterophily, BotBR incorporates an edge detector to differentiate between high-and low-reliability edges, optimizing the utilization of structural graph information.Furthermore, a homophily-based graph is introduced for consistency contrastive learning, enhancing the model's robustness.Experimental results on real-world bot detection benchmark datasets demonstrate that BotBR achieves state-of-the-art performance while maintaining efficiency comparable to classical methods.